Thus, in this paper, we first investigate the fundamental question of when and how clustering can help in obtaining accurate classifiers.
Our analysis yields novel insights into the benefits of using simple classifiers trained on clusters compared to simple or complex (in terms of Rademacher complexity [1]}) classifiers trained without clustering. It provides clues on when such a clustering-based approach may not improve the subsequent classification.
Finally, it also motivates the design of our clustering-based framework for classification, called Clustering Aware Classification (CAC).
